build_arm.md

Build TensorFlow Lite for ARM boards

This page describes how to build the TensorFlow Lite libraries for ARM-based computers.

TensorFlow Lite supports two build systems and supported features from each build system are not identical. Check the following table to pick a proper build system.

Feature	Bazel	CMake
Predefined toolchains	armhf, aarch64	armel, armhf, aarch64
Custom toolchains	harder to use	easy to use
Select TF ops	supported	not supported
GPU delegate	only available for Android	any platform that supports OpenCL
XNNPack	supported	supported
Python Wheel	supported	supported
C API	supported	supported
C++ API	supported for Bazel projects	supported for CMake projects

Cross-compilation for ARM with CMake

If you have a CMake project or if you want to use a custom toolchain, you'd better use CMake for cross compilation. There is a separate Cross compilation TensorFlow Lite with CMake page available for this.

Cross-compilation for ARM with Bazel

If you have a Bazel project or if you want to use TF ops, you'd better use Bazel build system. You'll use the integrated ARM GCC 8.3 toolchains with Bazel to build an ARM32/64 shared library.

Target Architecture	Bazel Configuration	Compatible Devices
armhf (ARM32)	--config=elinux_armhf	RPI3, RPI4 with 32 bit
: : : Raspberry Pi OS :
AArch64 (ARM64)	--config=elinux_aarch64	Coral, RPI4 with Ubuntu 64
: : : bit :

Note: The generated shared library requires glibc 2.28 or higher to run.

The following instructions have been tested on Ubuntu 16.04.3 64-bit PC (AMD64) and TensorFlow devel docker image tensorflow/tensorflow:devel.

To cross compile TensorFlow Lite with Bazel, follow the steps:

Step 1. Install Bazel

Bazel is the primary build system for TensorFlow. Install the latest version of the Bazel build system.

Note: If you're using the TensorFlow Docker image, Bazel is already available.

Step 2. Clone TensorFlow repository

git clone https://github.com/tensorflow/tensorflow.git tensorflow_src

Note: If you're using the TensorFlow Docker image, the repo is already provided in /tensorflow_src/.

Step 3. Build ARM binary

C library

bazel build --config=elinux_aarch64 -c opt //tensorflow/lite/c:libtensorflowlite_c.so

You can find a shared library in: bazel-bin/tensorflow/lite/c/libtensorflowlite_c.so.

Note: Use elinux_armhf for 32bit ARM hard float build.

Check TensorFlow Lite C API page for the detail.

C++ library

bazel build --config=elinux_aarch64 -c opt //tensorflow/lite:libtensorflowlite.so

You can find a shared library in: bazel-bin/tensorflow/lite/libtensorflowlite.so.

Currently, there is no straightforward way to extract all header files needed, so you must include all header files in tensorflow/lite/ from the TensorFlow repository. Additionally, you will need header files from FlatBuffers and Abseil.

Etc

You can also build other Bazel targets with the toolchain. Here are some useful targets.

• //tensorflow/lite/tools/benchmark:benchmark_model

• //tensorflow/lite/examples/label_image:label_image